AI-Proof Your Data Science Career With These 4 Skills

Have you heard that AI is coming to take our jobs? It’s automating, accelerating, and replacing more and more of what we do as data scientists.

If you're a data analyst, data scientist, machine learning engineer, or data engineer, you've probably asked yourself, “Is AI going to replace me?”. But here's the thing: many aspects of data jobs will always need the human touch.

Let's explore the future–proof skills that AI can't fully take over. We'll see how you can stay relevant in your data science career by mastering these skills and working with AI as your tool, not your master.

Skill #1: Use Your Domain Knowledge To Answer Analytical Questions And Use AI To Help You Write SQL Faster

SQL has been around for decades, and guess what? It's here to stay. Sure, AI can handle simple data queries, but complex data extraction? That requires the human touch. To get real insights, someone must understand what to look for and how to structure that search.

As the article on LogicLoop explains, AI can help you debug, edit, explain, and sometimes optimize your SQL queries. However, it fails terribly at writing complex SQL code relevant to your task.

This is because it doesn't have your specific data schema, nor does it know the business logic. Many companies are trying to solve this problem but still haven't. Even technology like GitHub Copilot, an AI assistant designed to help with writing code that has been trained on code from millions of GitHub users and their repos, isn't exactly great at writing SQL code.

Again, this is because it lacks access to data schema, as discussed in the article on HackerNoon.

So, knowing SQL and how to create complex queries will keep you relevant and make you the essential link between raw data and actionable insights. AI can be used to enhance your productivity and make your work go faster instead of replacing you.

Skill #2: AI Is Good at Repetitive Cleaning Tasks, But It Needs Your Expertise to Find Data Quality Issues

Here’s a fun fact: AI loves data but absolutely hates messy data. Machines rely on us to make sure data is clean, accurate, and meaningful.

Automation might handle repetitive cleaning tasks, but when it comes to overseeing data quality and checking for accuracy, humans are still in control. Automated data cleaning has several limitations, including:

1. Complexity of errors that require human judgment and domain expertise
2. Lack of contextual understanding
3. Over-reliance on rules, which can lead to false positives or false negatives
4. Struggling with cleaning unstructured data
5. Maintenance overhead for adapting to evolving data quality requirements
6. Scalability challenges when handling extremely large datasets

As you can see, not even the best AI can spot every inconsistency.

Data quality maintenance and automation oversight are the skills that will ensure AI isn't making decisions on bad data. You will make sure of that by working alongside AI tools, monitoring quality, and fixing issues AI can't detect.

Skill #3: AI Can Perform Mathematical & Model Optimizations, But You Optimize Their Optimization

Mathematical optimization is one of the most powerful future-proof data science skills. It's about finding the best solutions to complex problems – like optimizing logistics or cutting costs without sacrificing quality.

However, optimization models have their limitations, which are so eloquently described in the article on FasterCapital:

1. The complexity of systems and processes runs the risk of not being correctly captured by the model, leading to suboptimal solutions. One solution is to run sensitivity analyses.
2. As the optimization models depend on available data and its quality, collecting it, validating it, and cleansing it is a crucial task.
3. Model assumptions have to be evaluated against real-world data and expert knowledge.

To circumvent these limitations and optimize the model, you need skills in the fields of model evaluation methods, data collection and cleaning, and domain knowledge. AI can assist you, but it doesn't understand real-world complexities the way humans do.

This skill makes you indispensable in fields like supply chain management, finance, and more. Knowing how to optimize with human understanding using AI as a tool will make you the MVP for any data-driven project.

Skill #4: AI Can Help Implement ML Models, But Humans Are Needed To Interpret And Optimize The Outputs

As ML models become more advanced, understanding how they work under the hood will set you apart. AI can create models, but explaining why a model made a certain decision or troubleshooting unexpected outputs? That's on you.

The importance of model interpretability is nicely laid out in Christoph Molnar’s book “Interpretable Machine Learning”.

1. Interpretability is important because, for certain problems or tasks, a correct prediction (the what) is not enough, but knowing how the model came to the prediction (the why) is also required to solve the original problem. This is something called incompleteness in problem formalization by Doshi-Velez and Kim in their Cornell University paper “Towards A Rigorous Science of Interpretable Machine Learning”.
2. Interpretability helps make data be the source of knowledge, instead of black box machine learning models, by extracting the additional knowledge captured by the model.
3. Interpretability is also a useful debugging tool for detecting biases in ML models.

By understanding ML, you'll be the one tuning and refining these models. You'll work with AI, yes, but you will be the one explaining its decisions to business leaders and fine-tuning it for better performance.

Conclusion

The future of data jobs isn’t about fighting off AI but about working with it. To stay relevant, master these future-proof skills, leverage AI as a tool to make work faster and smarter, and be ready to add the human touch.